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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "import torch\n",
    "from torch import nn, optim\n",
    "from torch.autograd import Variable\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reading data\n",
    "TrainData = pd.read_table('Data/dataForTrainingLogistic.txt',header=None,delim_whitespace=True)\n",
    "TestData = pd.read_table('Data/dataForTestingLogistic.txt',header=None,delim_whitespace=True)\n",
    "#  print(type(data))\n",
    "# print(TrainData.describe())\n",
    "# print(TestData.describe())\n",
    "# TrainData.head(3)\n",
    "# TestData.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "(400, 7)\n(100, 7)\n"
     ]
    }
   ],
   "source": [
    "trainds = TrainData.values\n",
    "testds = TestData.values\n",
    "print(trainds.shape)\n",
    "print(testds.shape)\n",
    "trainsize = trainds.shape[1]\n",
    "testsize = testds.shape[1]\n",
    "if trainsize != testsize:\n",
    "    print(\"Different size for training and testing\")\n",
    "x_train = trainds[:,:(trainsize-1)]\n",
    "y_train = trainds[:,(trainsize-1)]\n",
    "x_test = testds[:,:(testsize-1)]\n",
    "y_test = testds[:,(testsize-1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "torch.Size([400, 6])\n"
     ]
    }
   ],
   "source": [
    "# build model for logistic regression\n",
    "class LogisticRegression(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.linear = nn.Linear(6, 1)\n",
    "        self.sigmoid = nn.Sigmoid()\n",
    " \n",
    "    def forward(self, x):\n",
    "        x = self.linear(x)\n",
    "        x = self.sigmoid(x)\n",
    "        return x\n",
    "x_train = torch.FloatTensor(x_train)\n",
    "y_train = torch.FloatTensor(y_train)\n",
    "y_train = y_train.reshape(-1,1)\n",
    "x_train = Variable(x_train)\n",
    "y_train = Variable(y_train)     \n",
    "\n",
    "x_test = torch.FloatTensor(x_test)\n",
    "y_test = torch.FloatTensor(y_test)\n",
    "y_test = y_test.reshape(-1,1)\n",
    "x_test = Variable(x_test)\n",
    "y_test = Variable(y_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "epoch 1000 , loss = 0.5429348349571228 , acc = 0.7875\n",
      "epoch 2000 , loss = 0.48950251936912537 , acc = 0.99\n",
      "epoch 3000 , loss = 0.4506809115409851 , acc = 0.9975\n",
      "epoch 4000 , loss = 0.41887134313583374 , acc = 1.0\n",
      "epoch 5000 , loss = 0.39152991771698 , acc = 1.0\n",
      "epoch 6000 , loss = 0.36756613850593567 , acc = 1.0\n",
      "epoch 7000 , loss = 0.3463539779186249 , acc = 1.0\n",
      "epoch 8000 , loss = 0.32745227217674255 , acc = 1.0\n",
      "epoch 9000 , loss = 0.3105182647705078 , acc = 1.0\n",
      "epoch 10000 , loss = 0.29527369141578674 , acc = 1.0\n",
      "epoch 11000 , loss = 0.28148892521858215 , acc = 1.0\n",
      "epoch 12000 , loss = 0.2689720392227173 , acc = 1.0\n",
      "epoch 13000 , loss = 0.2575621008872986 , acc = 1.0\n",
      "epoch 14000 , loss = 0.2471231073141098 , acc = 1.0\n",
      "epoch 15000 , loss = 0.2375396490097046 , acc = 1.0\n",
      "epoch 16000 , loss = 0.2287132292985916 , acc = 1.0\n",
      "epoch 17000 , loss = 0.22055938839912415 , acc = 1.0\n",
      "epoch 18000 , loss = 0.21300552785396576 , acc = 1.0\n",
      "epoch 19000 , loss = 0.20598866045475006 , acc = 1.0\n",
      "epoch 20000 , loss = 0.1994544267654419 , acc = 1.0\n",
      "epoch 21000 , loss = 0.1933550387620926 , acc = 1.0\n",
      "epoch 22000 , loss = 0.1876489818096161 , acc = 1.0\n",
      "epoch 23000 , loss = 0.18229955434799194 , acc = 1.0\n",
      "epoch 24000 , loss = 0.17727451026439667 , acc = 1.0\n",
      "epoch 25000 , loss = 0.17254535853862762 , acc = 1.0\n",
      "epoch 26000 , loss = 0.16808664798736572 , acc = 1.0\n",
      "epoch 27000 , loss = 0.16387592256069183 , acc = 1.0\n",
      "epoch 28000 , loss = 0.1598929464817047 , acc = 1.0\n",
      "epoch 29000 , loss = 0.1561199575662613 , acc = 1.0\n",
      "epoch 30000 , loss = 0.1525401771068573 , acc = 1.0\n",
      "epoch 31000 , loss = 0.14913935959339142 , acc = 1.0\n",
      "epoch 32000 , loss = 0.14590445160865784 , acc = 1.0\n",
      "epoch 33000 , loss = 0.1428232192993164 , acc = 1.0\n",
      "epoch 34000 , loss = 0.13988494873046875 , acc = 1.0\n",
      "epoch 35000 , loss = 0.13707979023456573 , acc = 1.0\n",
      "epoch 36000 , loss = 0.13439953327178955 , acc = 1.0\n",
      "epoch 37000 , loss = 0.13183435797691345 , acc = 1.0\n",
      "epoch 38000 , loss = 0.12937860190868378 , acc = 1.0\n",
      "epoch 39000 , loss = 0.12702441215515137 , acc = 1.0\n",
      "epoch 40000 , loss = 0.1247655600309372 , acc = 1.0\n",
      "epoch 41000 , loss = 0.12259633094072342 , acc = 1.0\n",
      "epoch 42000 , loss = 0.120511494576931 , acc = 1.0\n",
      "epoch 43000 , loss = 0.11850639432668686 , acc = 1.0\n",
      "epoch 44000 , loss = 0.11657610535621643 , acc = 1.0\n",
      "epoch 45000 , loss = 0.11471626907587051 , acc = 1.0\n",
      "epoch 46000 , loss = 0.11292323470115662 , acc = 1.0\n",
      "epoch 47000 , loss = 0.1111941710114479 , acc = 1.0\n",
      "epoch 48000 , loss = 0.10952452570199966 , acc = 1.0\n",
      "epoch 49000 , loss = 0.10791157931089401 , acc = 1.0\n",
      "epoch 50000 , loss = 0.10635260492563248 , acc = 1.0\n",
      "epoch 51000 , loss = 0.10484489798545837 , acc = 1.0\n",
      "epoch 52000 , loss = 0.10338541120290756 , acc = 1.0\n",
      "epoch 53000 , loss = 0.10197275131940842 , acc = 1.0\n",
      "epoch 54000 , loss = 0.1006036177277565 , acc = 1.0\n",
      "epoch 55000 , loss = 0.09927651286125183 , acc = 1.0\n",
      "epoch 56000 , loss = 0.09798929840326309 , acc = 1.0\n",
      "epoch 57000 , loss = 0.09674063324928284 , acc = 1.0\n",
      "epoch 58000 , loss = 0.09552853554487228 , acc = 1.0\n",
      "epoch 59000 , loss = 0.09435128420591354 , acc = 1.0\n",
      "epoch 60000 , loss = 0.0932069793343544 , acc = 1.0\n",
      "epoch 61000 , loss = 0.0920945554971695 , acc = 1.0\n",
      "epoch 62000 , loss = 0.09101291745901108 , acc = 1.0\n",
      "epoch 63000 , loss = 0.08996102213859558 , acc = 1.0\n",
      "epoch 64000 , loss = 0.0889367088675499 , acc = 1.0\n",
      "epoch 65000 , loss = 0.08793905377388 , acc = 1.0\n",
      "epoch 66000 , loss = 0.0869683176279068 , acc = 1.0\n",
      "epoch 67000 , loss = 0.08602141588926315 , acc = 1.0\n",
      "epoch 68000 , loss = 0.08509822189807892 , acc = 1.0\n",
      "epoch 69000 , loss = 0.0841994509100914 , acc = 1.0\n",
      "epoch 70000 , loss = 0.08332110196352005 , acc = 1.0\n",
      "epoch 71000 , loss = 0.08246495574712753 , acc = 1.0\n",
      "epoch 72000 , loss = 0.08162908256053925 , acc = 1.0\n",
      "epoch 73000 , loss = 0.08081278949975967 , acc = 1.0\n",
      "epoch 74000 , loss = 0.08001619577407837 , acc = 1.0\n",
      "epoch 75000 , loss = 0.07923615723848343 , acc = 1.0\n",
      "epoch 76000 , loss = 0.0784757062792778 , acc = 1.0\n",
      "epoch 77000 , loss = 0.07773140072822571 , acc = 1.0\n",
      "epoch 78000 , loss = 0.0770043432712555 , acc = 1.0\n",
      "epoch 79000 , loss = 0.0762917622923851 , acc = 1.0\n",
      "epoch 80000 , loss = 0.0755968913435936 , acc = 1.0\n",
      "epoch 81000 , loss = 0.07491431385278702 , acc = 1.0\n",
      "epoch 82000 , loss = 0.07424798607826233 , acc = 1.0\n",
      "epoch 83000 , loss = 0.07359523326158524 , acc = 1.0\n",
      "epoch 84000 , loss = 0.07295472174882889 , acc = 1.0\n",
      "epoch 85000 , loss = 0.07232967019081116 , acc = 1.0\n",
      "epoch 86000 , loss = 0.0717143788933754 , acc = 1.0\n",
      "epoch 87000 , loss = 0.07111410051584244 , acc = 1.0\n",
      "epoch 88000 , loss = 0.0705234706401825 , acc = 1.0\n",
      "epoch 89000 , loss = 0.06994568556547165 , acc = 1.0\n",
      "epoch 90000 , loss = 0.06937887519598007 , acc = 1.0\n",
      "epoch 91000 , loss = 0.06882283091545105 , acc = 1.0\n",
      "epoch 92000 , loss = 0.06827685236930847 , acc = 1.0\n",
      "epoch 93000 , loss = 0.06774204969406128 , acc = 1.0\n",
      "epoch 94000 , loss = 0.06721598654985428 , acc = 1.0\n",
      "epoch 95000 , loss = 0.06670072674751282 , acc = 1.0\n",
      "epoch 96000 , loss = 0.06619340181350708 , acc = 1.0\n",
      "epoch 97000 , loss = 0.06569724529981613 , acc = 1.0\n",
      "epoch 98000 , loss = 0.06520736962556839 , acc = 1.0\n",
      "epoch 99000 , loss = 0.06472877413034439 , acc = 1.0\n",
      "epoch 100000 , loss = 0.06425674259662628 , acc = 1.0\n",
      "The testing dataset include 100 examples,the num of misclassified examples is 0\n"
     ]
    }
   ],
   "source": [
    "logistic_model = LogisticRegression()\n",
    "criterion = nn.BCELoss()\n",
    "optimizer = optim.SGD(logistic_model.parameters(),lr=1e-3)\n",
    "\n",
    "epochnum = 100000\n",
    "trainloss_arr = []\n",
    "trainacc_arr = []\n",
    "testloss_arr = []\n",
    "testacc_arr = []\n",
    "# train logistic model for the task\n",
    "for epoch in range(epochnum):\n",
    "    out = logistic_model(x_train)\n",
    "    loss = criterion(out,y_train)\n",
    "    train_loss = loss.data.item() # get exact loss\n",
    "    mask = out.ge(0.5).float()\n",
    "    correct = (mask == y_train).sum()\n",
    "    acc = correct.data.item()/x_train.size(0)\n",
    "    optimizer.zero_grad()\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "    if((epoch+1) % 1000 == 0):\n",
    "        print('epoch {} , loss = {} , acc = {}'.format((epoch+1), train_loss, acc))\n",
    "        trainloss_arr.append(train_loss)\n",
    "        trainacc_arr.append(acc)\n",
    "        # 每1000次看一下在测试集上的结果\n",
    "        predictout = logistic_model(x_test)\n",
    "        predictloss = criterion(predictout,y_test)\n",
    "        test_loss = predictloss.data.item()\n",
    "        mask = predictout.ge(0.5).float()\n",
    "        correct = (mask == y_test).sum()\n",
    "        test_acc = correct.data.item()/x_test.size(0)\n",
    "        testloss_arr.append(test_loss)\n",
    "        testacc_arr.append(test_acc)\n",
    "\n",
    "# final misclassified examples\n",
    "predictout = logistic_model(x_test)\n",
    "mask = predictout.ge(0.5).float()\n",
    "correct = (mask == y_test).sum()\n",
    "print(\"The testing dataset include {} examples,the num of misclassified examples is {}\".format(len(x_test),(len(x_test)-correct)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.title('The loss of stochastic gradient descent')\n",
    "plt.xlabel('Epoch number')\n",
    "plt.ylabel('loss result')\n",
    "plt.plot(trainloss_arr,label = 'training loss')\n",
    "plt.plot(testloss_arr,label = 'testing loss')\n",
    "plt.legend(loc=0,ncol=2)\n",
    "# print(trainloss_arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x28c77e27160>"
      ]
     },
     "metadata": {},
     "execution_count": 109
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.title('The accuracy rate of the model')\n",
    "plt.xlabel('Epoch number')\n",
    "plt.ylabel('Accuracy rate')\n",
    "plt.plot(trainacc_arr,color='blueviolet',label = 'training dataset')\n",
    "plt.plot(testacc_arr,label = \"testing dataset\")\n",
    "plt.legend(loc=0,ncol=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "TrainLossArr = []\n",
    "TestLossArr = []\n",
    "Trainwrongrate = []\n",
    "Testwrongrate = []\n",
    "newx_train = []\n",
    "newy_train = []\n",
    "for i in range(40):\n",
    "    size = (i+1)*10\n",
    "    newx_train = []\n",
    "    newy_train = []    \n",
    "    for i in range(size):\n",
    "        temp = np.random.randint(0,len(x_train))\n",
    "        samplex = trainds[temp,:(trainsize-1)]\n",
    "        sampley = trainds[temp,(trainsize-1)]\n",
    "        newx_train.append(samplex)\n",
    "        newy_train.append(sampley)\n",
    "    newx_train = torch.FloatTensor(newx_train)\n",
    "    newy_train = torch.FloatTensor(newy_train)\n",
    "    newy_train =newy_train.reshape(-1,1)\n",
    "    newx_train = Variable(newx_train)\n",
    "    newy_train = Variable(newy_train)  \n",
    "    # train model\n",
    "    for epoch in range(1000):\n",
    "        out = logistic_model(newx_train)\n",
    "        loss = criterion(out,newy_train)\n",
    "        train_loss = loss.data.item() # get exact loss\n",
    "        mask = out.ge(0.5).float()\n",
    "        correct = (mask == newy_train).sum()\n",
    "        acc = correct.data.item()/newx_train.size(0)\n",
    "        wrong = 1-acc\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "    # add LossArr\n",
    "    TrainLossArr.append(train_loss)\n",
    "    Trainwrongrate.append(wrong)\n",
    "    # test loss\n",
    "    predictout = logistic_model(x_test)\n",
    "    predictloss = criterion(predictout,y_test)\n",
    "    test_loss = predictloss.data.item()\n",
    "    mask = predictout.ge(0.5).float()\n",
    "    correct = (mask == y_test).sum()\n",
    "    test_acc = correct.data.item()/x_test.size(0)\n",
    "    test_wrong = 1-test_acc\n",
    "    TestLossArr.append(test_loss)\n",
    "    Testwrongrate.append(test_wrong)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x28c782a6700>"
      ]
     },
     "metadata": {},
     "execution_count": 135
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.title('The loss of stochastic gradient descent')\n",
    "plt.xlabel('Epoch number')\n",
    "plt.ylabel('loss result')\n",
    "plt.plot(TrainLossArr,label = 'training loss')\n",
    "plt.plot(TestLossArr,label = 'testing loss')\n",
    "plt.legend(loc=0,ncol=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.title('Error rate compared')\n",
    "plt.xlabel('Sample number')\n",
    "plt.ylabel('Error rate')\n",
    "plt.plot(TrainLossArr,label = 'training error rate')\n",
    "plt.plot(TestLossArr,label = 'testing error rate')\n",
    "plt.legend(loc=0,ncol=2)"
   ]
  }
 ]
}